Hand Pose Estimation in the Task of Egocentric Actions

Hrúz, Marek; Kanis, Jakub; Krňoul, Zdeněk

doi:10.1109/access.2021.3050624

Cited by 2 publications

(1 citation statement)

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“…These are mostly known as ensemble models researched on classification tasks. In [31], the effect of ensemble components and ensemble cardinality in a regression task at several levels increases the estimation accuracy of the egocentric hand pose estimation task.…”

Section: Ensemblementmentioning

confidence: 99%

One Model is Not Enough: Ensembles for Isolated Sign Language Recognition

Hrúz

Gruber

Kanis

et al. 2022

Sensors

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In this paper, we dive into sign language recognition, focusing on the recognition of isolated signs. The task is defined as a classification problem, where a sequence of frames (i.e., images) is recognized as one of the given sign language glosses. We analyze two appearance-based approaches, I3D and TimeSformer, and one pose-based approach, SPOTER. The appearance-based approaches are trained on a few different data modalities, whereas the performance of SPOTER is evaluated on different types of preprocessing. All the methods are tested on two publicly available datasets: AUTSL and WLASL300. We experiment with ensemble techniques to achieve new state-of-the-art results of 73.84% accuracy on the WLASL300 dataset by using the CMA-ES optimization method to find the best ensemble weight parameters. Furthermore, we present an ensembling technique based on the Transformer model, which we call Neural Ensembler.

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Section: Ensemblementmentioning

confidence: 99%